Building Hiver’s Data Landscape prototype in a weekend

Background

In today’s data-driven world, having a clear, comprehensive view of your company’s data is crucial for making informed business decisions. At Hiver, we faced a common challenge: our data was scattered across various sources like Gainsight, Stripe, HubSpot, Hiver Databases and other platforms. This fragmentation made it difficult to gain a holistic view of our operations, understand customer behaviors, and identify trends. To address this, we embarked on a project titled “Hiver’s Data Landscape” aimed at consolidating our data into a unified system for better accessibility and visualization.

The Challenge

Our data ecosystem was fragmented, with critical information residing in disparate sources.

This scattered data landscape posed several problems:

• Data Inaccessibility: Retrieving data required accessing multiple platforms, each with its own interface and query mechanisms.
• Inefficiency: Manual data extraction and consolidation were time-consuming and error-prone.
• Limited Insights: The fragmented data prevented us from getting a comprehensive view of our operations, hindering our ability to make data-driven decisions.

The Solution: Hiver’s Data Landscape

To overcome these challenges, we aimed to build a robust data infrastructure quickly, leveraging readily available technology. Our solution integrated various tools to streamline data collection, processing, storage, and visualization.

Hiver’s Data Landscape Prototype HLD

ETL with AWS Glue

We used AWS Glue for our ETL (Extract, Transform, Load) pipelines. AWS Glue enabled us to:

• Extract data from multiple sources efficiently.
• Transform the data to ensure consistency and quality.
• Load the cleaned data into a central repository.
• Visual ETL Builder: AWS Glue’s visual ETL builder allowed us to design and manage our ETL workflows with ease, using a drag-and-drop interface that simplified the process of creating, monitoring, and managing data flows.

Data Storage with Amazon S3

For permanent storage, we chose Amazon S3. S3’s cost-effectiveness and scalability made it an ideal choice. Key benefits included:

• Cost Savings: We only pay for the storage we use, helping us manage costs effectively.
• Scalability: S3 scales seamlessly as our data grows, ensuring we never run out of storage space.

Querying with Amazon Athena

To query our data, we utilized Amazon Athena. Athena’s serverless architecture and pay-per-query model provided several advantages:

• Cost Efficiency: We only pay for the data scanned by queries, keeping costs in check.
• Ease of Use: Athena integrates directly with S3, allowing us to run SQL queries on our data without needing to set up and manage infrastructure.

Visualization with Apache Superset

For visualization, we implemented Apache Superset. Superset’s powerful visualization capabilities allowed us to:

• Create Interactive Dashboards: We built dynamic dashboards that provide real-time insights.
• Enable Data Exploration: Users across the organization can explore data and generate reports easily.

The Impact

Implementing Hiver’s Data Landscape has transformed our data operations. The key impacts include:

• Enhanced Data Visibility: Data is now accessible across the organization, enabling better collaboration and decision-making.
• Insightful Analytics: We can query and analyze data to understand which features of Hiver are most used, identify customer usage patterns, and track feature adoption trends.
• Informed Decisions: The insights derived from our data help drive important business decisions, improving our overall strategic direction.

Conclusion

Hiver’s Data Landscape has successfully unified our scattered data sources into a cohesive, efficient system. By leveraging AWS Glue, Amazon S3, Amazon Athena, and Apache Superset, we have created a powerful data infrastructure that enhances visibility, efficiency, and decision-making capabilities across our organization. This project demonstrates the value of integrating modern, scalable technologies to solve complex data challenges and drive business growth.

Looking ahead, we plan to further enhance our data landscape by incorporating AI and machine learning (ML) technologies. By leveraging AI-ML, we aim to uncover deeper insights, predict trends, and automate decision-making processes, driving even greater value from our data. We are excited about the potential of AI-ML to revolutionize our data analytics, enabling us to stay ahead of the curve and continue propelling Hiver forward.

Join us

At Hiver, we’re not just sharing emails; we’re building the future of communication with technology that bridges gaps and brings people closer, no matter where they are.

If you’re excited by the prospect of solving complex problems, diving deep into the world of distributed systems, and making a tangible impact on the efficiency and reliability of email sharing workflows, we would love to hear from you. We believe in fostering a culture where creativity meets technology, and where individual contributions are valued and celebrated.

Discover the opportunities waiting for you at Hiver by visiting our careers page. Whether you’re a seasoned developer or just starting your journey in tech, we have a place for you. Together, we can shape the future of communication, one email at a time.